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Found 12 papers, 2 papers with code
Disaster mapping from satellites: damage detection with crowdsourced point labels
High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to access routes.
Using Echo State Networks to Approximate Value Functions for Control
An Echo State Network (ESN) is a type of single-layer recurrent neural network with randomly-chosen internal weights and a trainable output layer.
Mining and Tailings Dam Detection In Satellite Imagery Using Deep Learning
This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyse a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil.
BCCNet: Bayesian classifier combination neural network
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements.
Uncertainty propagation in neural networks for sparse coding
A novel method to propagate uncertainty through the soft-thresholding nonlinearity is proposed in this paper.
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference.
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning
The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions.
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences
Video analytics requires operating with large amounts of data.
Structured Sparse Modelling with Hierarchical GP
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed.
Learning Methods for Dynamic Topic Modeling in Automated Behaviour Analysis
Semi-supervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators load.
Anomaly detection in video with Bayesian nonparametrics
A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper.
Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video
The proposed method is compared with the method based on the non- dynamic Hierarchical Dirichlet Process, for which we also derive the online Gibbs sampler and the abnormality measure.
